TWM616670U - A system of assisted interpretation of bone medical images with artificial intelligence - Google Patents
A system of assisted interpretation of bone medical images with artificial intelligence Download PDFInfo
- Publication number
- TWM616670U TWM616670U TW109215099U TW109215099U TWM616670U TW M616670 U TWM616670 U TW M616670U TW 109215099 U TW109215099 U TW 109215099U TW 109215099 U TW109215099 U TW 109215099U TW M616670 U TWM616670 U TW M616670U
- Authority
- TW
- Taiwan
- Prior art keywords
- medical image
- bone
- medical
- types
- artificial intelligence
- Prior art date
Links
Images
Landscapes
- Image Analysis (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
Description
本創作為一種智慧醫療的技術領域，特別是一種針對骨頭輔助影像識別的人工智慧輔助骨頭醫學影像判讀系統。 This creation is a technical field of intelligent medical treatment, especially an artificial intelligence-assisted bone medical image interpretation system for bone-assisted image recognition.
傳統上，醫生要檢查骨頭的狀態，需要透過例如X光或MRI進行影像判斷；然而，骨頭上細微的損傷是難以從X光或MRI的影像中判別出來的，這是骨科及手外科醫師共同的痛點。 Traditionally, doctors need to check the condition of bones by imaging such as X-ray or MRI. However, it is difficult to distinguish minor damage on bones from X-ray or MRI images. This is common to orthopedics and hand surgeons. Pain points.
舉例而言，腕部之三角纖維軟骨複合體(triangular fibrocartilage complex，TFCC)是介於遠端尺骨、遠端橈骨以及尺側掌骨之間的構造，其包含了韌帶及軟骨組織，扮演著穩定遠端橈尺骨關節的重要角色腕部三角纖維軟骨複合體損傷好發於手腕部姿勢呈旋前(pronation)以及過度伸張(hyperextension)的動作，也可能合併遠端橈骨骨折。較常見的球類運動傷害包括網球以及羽毛球。 For example, the triangular fibrocartilage complex (TFCC) of the wrist is a structure between the distal ulna, the distal radius and the ulnar metacarpal bone. An important role of the distal radioulnar joint. Triangular fibrocartilage complex injuries of the wrist are more likely to occur in pronation and hyperextension of the wrist posture, and may also be combined with distal radius fractures. The more common ball sports injuries include tennis and badminton.
目前三角纖維軟骨複合體的損傷診斷以醫學影像為主，例如X光可查看尺骨差異(ulnar variance，「正值」尺骨差異可能代表了TFCC所在的空隙被壓縮，可當作診斷三角纖維軟骨複合體病變的參考，另外亦可觀察週遭掌骨是否有軟骨軟化或是關節炎現象)；以及，核磁共振影像(Magnetic Resonance Imaging)主要觀察三角纖維軟骨複合體在尺側或是橈側附著點有無破裂情形。 At present, the diagnosis of triangular fibrocartilage complex damage is mainly based on medical imaging. For example, X-ray can see the ulnar variance (ulnar variance, "positive value" ulnar variance, which may represent the compression of the space where TFCC is located, which can be used as a diagnosis of triangular fibrocartilage complex). Reference for body lesions, in addition to observing whether there is cartilage softening or arthritis in the surrounding metacarpal bones); and, Magnetic Resonance Imaging (Magnetic Resonance Imaging) mainly observes whether the triangular fibrocartilage complex has rupture at the ulnar or radial attachment point .
由於三角纖維軟骨複合體構造複雜且潛在病變繁多，有時核磁共振影像也無法判定是否有受損。現有的一些研究論文針對如何使用影像改善核磁共振影像損傷的臨床判讀被發表，足以證明TFCC損傷判讀困難是骨科醫師共同的困擾。 Due to the complex structure of the triangular fibrocartilage complex and the many potential diseases, sometimes the MRI cannot determine whether it is damaged. Some existing research papers have been published on how to use images to improve the clinical interpretation of MRI damage, which is enough to prove that the difficulty in interpreting TFCC damage is a common problem for orthopedic physicians.
有鑑於此，本創作提出一種人工智慧輔助骨頭醫學影像判讀方法及其系統，其用以解決習知技術的缺失。 In view of this, this creation proposes an artificial intelligence-assisted bone medical image interpretation method and its system to solve the lack of conventional technology.
本創作之第一目的係提供一種人工智慧輔助骨頭醫學影像判讀方法，以人工智慧的影像識別技術，以輔助醫師標註出骨頭上細微的損傷的位置及嚴重程度。 The first purpose of this creation is to provide an artificial intelligence-assisted bone medical image interpretation method, using artificial intelligence image recognition technology to assist doctors in marking the location and severity of subtle bone injuries.
本創作之第二目的係根據上述人工智慧輔助骨頭醫學影像判讀方法，可以進行手腕之三角纖維軟骨(triangular fibrocartilage complex)的損傷預測與分類。 The second purpose of this creation is to predict and classify the triangular fibrocartilage complex damage of the wrist based on the above-mentioned artificial intelligence-assisted bone medical image interpretation method.
本創作之第三目的係根據上述人工智慧輔助骨頭醫學影像判讀方法，可以進行骨頭年齡的預測。 The third purpose of this creation is to predict the age of bones based on the aforementioned artificial intelligence-assisted bone medical image interpretation method.
本創作之第四目的係根據上述人工智慧輔助骨頭醫學影像判讀方法，可以利用現已存在的醫學影像且經過判定的的結果作為醫學樣本影像，以訓練深度學習演算法。 The fourth purpose of this creation is based on the aforementioned artificial intelligence-assisted bone medical image interpretation method, which can use existing medical images and the determined results as medical sample images to train deep learning algorithms.
本創作之第五目的係根據上述人工智慧輔助骨頭醫學影像判讀方法，藉由深度學習演算法建立類型，以將骨頭的類型區分為正常類型與非正常類型，且在非正常的部分也可以導入例如帕爾默分類(palmer classification)。 The fifth purpose of this creation is based on the above-mentioned artificial intelligence-assisted bone medical image interpretation method, and the deep learning algorithm is used to create types to distinguish the types of bones into normal and abnormal types, and the abnormal parts can also be imported For example, Palmer classification.
本創作之第六目的係根據上述人工智慧輔助骨頭醫學影像判讀方法，提供包含影像編號與該真/偽確認欄之複核確認表單，以讓醫生根據影像編號在複核確認表單紀錄相應影像編號之醫學樣本影像之複核結果，以達到優化深度學習演算法的目的。 The sixth purpose of this creation is to provide a review confirmation form containing the image number and the authenticity/false confirmation column based on the above artificial intelligence-assisted bone medical image interpretation method, so that the doctor can record the corresponding image number in the review confirmation form according to the image number. The results of the review of the sample images to achieve the purpose of optimizing the deep learning algorithm.
本創作之第七目的係提供一種人工智慧輔助骨頭醫學影像判讀系統，係用於實現人工智慧輔助骨頭醫學影像判讀方法。 The seventh purpose of this creation is to provide an artificial intelligence-assisted bone medical image interpretation system, which is used to realize artificial intelligence-assisted bone medical image interpretation methods.
為達到上述目的與其他目的，本創作提供一種人工智慧輔助骨頭醫學影像判讀方法。人工智慧輔助骨頭醫學影像判讀方法包含步驟S1，係接收一醫學影像，其中醫學影像相關於一骨頭；步驟S2，係執行一深度學習演算法(deep learn algorithm)演算醫學影像，以自醫學影像擷取一特徵點(feature point)而建立一分布狀態；步驟S3，係執行該深度學習演算法，以根據分布狀態自複數類型選擇一個或多個類型；步驟S4，係利用一熱點影像演算法演算醫學影像，以在醫學影像標記骨頭之一指定部位；以及步驟S5，係顯示經標記的指定部位的醫學影像及其相應的該類型，以供提供一輔助診斷資訊。 In order to achieve the above and other purposes, this creation provides an artificial intelligence-assisted bone medical image interpretation method. The artificial intelligence-assisted bone medical image interpretation method includes step S1, receiving a medical image, wherein the medical image is related to a bone; step S2, executing a deep learning algorithm (deep learn algorithm) to calculate the medical image to extract the medical image Take a feature point to establish a distribution state; Step S3 is to execute the deep learning algorithm to select one or more types from the complex number type according to the distribution state; Step S4 is to use a hotspot image algorithm to calculate The medical image is used to mark a designated part of the bone in the medical image; and step S5 is to display the marked medical image of the designated part and its corresponding type to provide auxiliary diagnostic information.
為達到上述目的與其他目的，本創作提供一種人工智慧輔助骨頭醫學影像判讀系統，係包含一輸入單元、一處理單元與一輸出單元。輸入單元接 收一醫學影像，其中醫學影像相關於一骨頭。處理單元連接輸入單元。處理單元執行一深度學習演算法以演算醫學影像，而自醫學影像擷取一特徵點以建立一分布狀態，且深度學習演算法又根據分布狀態自複數類型選擇一個或多個類型，另處理單元執行一熱點影像演算法演算醫學影像，以在醫學影像標記骨頭之一指定部位。輸出單元連接處理單元。輸出單元輸出已標記的骨頭之指定部位的醫學影像與被選擇的類型或等類型。 In order to achieve the above and other objectives, this creation provides an artificial intelligence-assisted bone medical image interpretation system, which includes an input unit, a processing unit and an output unit. Input unit connection Receive a medical image, where the medical image is related to a bone. The processing unit is connected to the input unit. The processing unit executes a deep learning algorithm to calculate the medical image, and extracts a feature point from the medical image to establish a distribution state, and the deep learning algorithm selects one or more types from the plural types according to the distribution state, and another processing unit Perform a hotspot image algorithm to calculate the medical image to mark a designated part of the bone in the medical image. The output unit is connected to the processing unit. The output unit outputs the medical image of the designated part of the marked bone and the selected type or the like.
相較於習知的技術，本創作提供的人工智慧輔助骨頭醫學影像判讀方法及其系統，能夠針對骨頭上細微損傷進行影像判斷、檢傷分類與損傷病灶標示等功能，以輔助醫生進行醫療診斷；另外，醫生也可以針對本創作的內容進行反饋以增強人工智慧的判斷能力。 Compared with the conventional technology, the artificial intelligence-assisted bone medical image interpretation method and system provided by this creation can perform image judgment, inspection classification, and injury lesion labeling functions for minor injuries on the bones to assist doctors in medical diagnosis. ; In addition, doctors can also give feedback on the content of this creation to enhance the ability of artificial intelligence to judge.
S11-S15:方法步驟 S11-S15: Method steps
S31-S37:方法步驟 S31-S37: Method steps
2:骨頭 2: bones
22:指定部位 22: Designated part
10、10’:人工智慧輔助骨頭醫學影像判讀系統 10, 10’: Artificial intelligence assisted bone medical image interpretation system
12:輸入單元 12: Input unit
14:處理單元 14: Processing unit
16:輸出單元 16: output unit
18:回饋模組 18: Feedback module
MIMG、MIMG':醫學影像 MIMG, MIMG': medical imaging
DLA:深度學習演算法 DLA: Deep Learning Algorithm
CLS:類型 CLS: Type
HPA:熱點影像演算法 HPA: Hotspot image algorithm
圖1係本創作第一實施例之人工智慧輔助骨頭醫學影像判讀方法的流程示意圖。 FIG. 1 is a schematic flowchart of the artificial intelligence-assisted bone medical image interpretation method of the first embodiment of the present creation.
圖2(a)係說明本創作圖1之骨頭的X光示意圖。 Figure 2(a) is an X-ray schematic diagram illustrating the bones of Figure 1 of this creation.
圖2(b)係說明本創作圖3之經標記的手骨X光醫療影像。 Figure 2(b) illustrates the marked hand bone X-ray medical image of Figure 3 of this creation.
圖3係說明本創作之訓練該深度學習演算法的流程示意圖。 Figure 3 is a schematic diagram illustrating the process of training the deep learning algorithm of this creation.
圖4係說明本創作圖3之訓練該深度學習演算法的結構示意圖。 Fig. 4 is a schematic diagram illustrating the structure of training the deep learning algorithm of Fig. 3 of the present creation.
圖5係說明本創作圖1之機率分布分佈在各類型的狀態示意圖。 Fig. 5 is a schematic diagram illustrating the distribution of the probability distribution in each type of the creation of Fig. 1.
圖6係本創作第二實施例之人工智慧輔助骨頭醫學影像判讀系統的方塊圖。 Fig. 6 is a block diagram of the artificial intelligence-assisted bone medical image interpretation system of the second embodiment of the present creation.
圖7係本創作第三實施例之人工智慧輔助骨頭醫學影像判讀系統的方塊圖。 FIG. 7 is a block diagram of the artificial intelligence assisted bone medical image interpretation system of the third embodiment of the present creation.
為充分瞭解本創作之目的、特徵及功效，茲藉由下述具體之實施例，並配合所附之圖式，對本創作做一詳細說明，說明如後。 In order to fully understand the purpose, features, and effects of this creation, the following specific embodiments are used in conjunction with the accompanying drawings to give a detailed description of this creation, which will be described later.
於本創作中，係使用「一」或「一個」來描述本文所述的單元、元件和組件。此舉只是為了方便說明，並且對本創作之範疇提供一般性的意義。因此，除非很明顯地另指他意，否則此種描述應理解為包括一個、至少一個，且單數也同時包括複數。 In this creation, "one" or "one" is used to describe the units, components, and components described herein. This is just for the convenience of explanation and to provide a general meaning to the scope of this creation. Therefore, unless it is clearly stated otherwise, this description should be understood to include one or at least one, and the singular number also includes the plural number.
於本創作中，用語「包含」、「包括」、「具有」、「含有」或其他任何類似用語意欲涵蓋非排他性的包括物。舉例而言，含有複數要件的一元件、結構、製品或裝置不僅限於本文所列出的此等要件而已，而是可以包括未明確列出但卻是該元件、結構、製品或裝置通常固有的其他要件。除此之外，除非有相反的明確說明，用語「或」是指涵括性的「或」，而不是指排他性的「或」。 In this creation, the terms "include", "include", "have", "include" or any other similar terms are intended to cover non-exclusive inclusions. For example, an element, structure, product, or device that contains a plurality of elements is not limited to the elements listed herein, but may include those that are not explicitly listed but are generally inherent to the element, structure, product, or device. Other requirements. In addition, unless there is a clear statement to the contrary, the term "or" refers to the inclusive "or" rather than the exclusive "or".
於本創作中，關於步驟的描述不因受到前後描述的順序而限制，根據本創作揭露的步驟主要是便於說明實施例，在其他的實施例中，執行步驟的順利可以調整或是變動。 In this creation, the description of the steps is not limited by the order of the preceding and following descriptions. The steps disclosed according to this creation are mainly to facilitate the description of the embodiments. In other embodiments, the smooth execution of the steps can be adjusted or changed.
請參考圖1，係本創作第一實施例之人工智慧輔助骨頭醫學影像判讀方法的流程示意圖。在圖1中，人工智慧輔助骨頭醫學影像判讀方法起始於步驟S11，係接收一醫學影像，例如醫學影像相關於一骨頭，例如手骨、頭骨、腳骨、髖關骨等。又，醫學影像可產生自X光機、X射線斷層影像(Computed Tomography)、核磁共振影像(Magnetic Resonance Imaging)或是來自於醫療影像資料庫。舉例而言，於本實施例中，骨頭係以手骨為例，可以一併參考圖2(a)的手骨X光醫療影像。在圖2(a)中，係說明本創作圖1之骨頭的X光示意圖。 Please refer to FIG. 1, which is a schematic flowchart of the artificial intelligence-assisted bone medical image interpretation method according to the first embodiment of the present creation. In FIG. 1, the artificial intelligence-assisted bone medical image interpretation method starts at step S11, which is to receive a medical image, for example, the medical image is related to a bone, such as hand bones, skulls, foot bones, hip bones, etc. In addition, medical images can be generated from X-ray machines, X-ray tomography (Computed Tomography), Magnetic Resonance Imaging (Magnetic Resonance Imaging) or from a medical image database. For example, in this embodiment, the bone is taken as an example of the hand bone, and the X-ray medical image of the hand bone in FIG. 2(a) can be referred to. In Figure 2(a), an X-ray schematic diagram of the bone in Figure 1 of this creation is illustrated.
在執行步驟S11之前，可以先執行訓練深度學習演算法的步驟，一併可以參考圖3，係說明本創作之訓練該深度學習演算法的流程示意圖。在圖3中，訓練該深度學習演算法的步驟係起始於S31，係提供複數醫學樣本影像。其中，該等醫學樣本影像相關於骨頭，於此是對應步驟S11對應的影像類型。 Before performing step S11, the step of training the deep learning algorithm can be performed first, and you can also refer to FIG. 3, which is a schematic diagram illustrating the process of training the deep learning algorithm of this creation. In Fig. 3, the step of training the deep learning algorithm starts at S31, which is to provide plural medical sample images. Among them, the medical sample images are related to bones, which correspond to the image types corresponding to step S11.
步驟S32，係分類該等醫學樣本影像，以將該等醫學樣本影像區分為複數類型，一併可以參照圖4，係說明本創作圖3之訓練該深度學習演算法的結構示意圖。在圖4中，該等類型是定義為非正常類型的1A、1B、1C與1D和正常類型。
Step S32 is to classify the medical sample images to classify the medical sample images into plural types. Refer also to FIG. 4, which is a schematic diagram illustrating the structure of training the deep learning algorithm in FIG. 3 of the present creation. In Figure 4, these types are defined as
步驟S33，係利用深度學習演算法演算每一該等類型對應的該等醫學樣本影像，在每一該等類型的等醫學樣本影像擷取特徵點(feature point)。 In step S33, a deep learning algorithm is used to calculate the medical sample images corresponding to each of these types, and feature points are extracted from each of the medical sample images of these types.
步驟S34，係紀錄特徵點所產生對應的分布狀態，使得非正常類型的Type 1A、Type 1B、Type 1C與Type 1D和正常類型都有其對應的分布狀態。其中，於本實施例中，非正常類型的數量係以4種為例說明，於其他實施例中，其非正常類型的數量可以少於4種或是多於4種，不受限於4種。又於另一實施例中，非正常類型可採用帕爾默分類(palmer classification)。帕爾默分類可進一步區分為外傷(traumatic injury)與退化性損傷(degenerative injury)等多個類型。
Step S34 is to record the corresponding distribution states of the feature points, so that the
步驟S35，係在該等醫學樣本影像標記骨頭之指定部位，例如指定部位可以為手腕之三角纖維軟骨(triangular fibrocartilage complex)。一併參考圖2(b)的經標記的手骨X光醫療影像。圖2(b)係說明本創作圖2(a)之已標記骨頭的X光示意圖。在圖2(b)中，手腕之三角纖維軟骨是透過例如熱點影像演算法演算，而能夠在手骨X光醫療影像以框框的方式標記出三角纖維軟骨。其中，熱點影像 演算法可以是利用基於梯度的敏感性分析(Gradient-based sensitivity analysis)及其他可解釋AI的演算法進行。 Step S35 is to mark the designated parts of the bones on the medical sample images. For example, the designated part may be the triangular fibrocartilage complex of the wrist. Also refer to the marked hand bone X-ray medical image in Figure 2(b). Figure 2(b) is an X-ray diagram illustrating the marked bone in Figure 2(a) of this creation. In Figure 2(b), the triangular fibrocartilage of the wrist is calculated by, for example, a hotspot image algorithm, and the triangular fibrocartilage can be marked in a frame on the X-ray medical image of the hand bone. Among them, the hot image The algorithm can be performed by using gradient-based sensitivity analysis and other interpretable AI algorithms.
值得注意的是，於另外一實施例中，深度學習演算法更可導入影響因子進行演算，例如影響因子可為年齡、性別、BMI、疾病史、手術史，該影像因子關聯於醫療影像的被照射者。 It is worth noting that, in another embodiment, the deep learning algorithm can further import impact factors for calculation. For example, the impact factors can be age, gender, BMI, disease history, and surgical history. The image factor is associated with the medical image. Irradiated.
回到圖3，步驟S36，係隨機地挑選預定數量的該等醫學樣本影像，以供醫生複核而產生該等醫學樣本影像的複核結果。其中，該等醫學樣本影像為已標記有指定部位。參照圖4，係顯示複核結果提供該等醫學樣本影像的影像編號與其相應的真(T)/偽(F)確認欄，以供醫生確認其醫學樣本影像經過深度學習演算法演算之後，其結果是否可信任。舉例而言，於本實施例中，若醫學樣本影像經過深度學習演算法演算顯示正確的類型，則醫生在真(T)/偽(F)確認欄勾選醫學樣本影像分類為真(T)；反之，則醫生在真(T)/偽(F)確認欄勾選醫學樣本影像分類為偽(F)。又於本實施例中，該等醫學樣本影像之預定數量可設定為至少100個且在預定數量中具有至少50個正常類型該等醫學樣本影像。 Returning to FIG. 3, step S36 is to randomly select a predetermined number of the medical sample images for review by the doctor to generate a review result of the medical sample images. Among them, the medical sample images are marked with designated parts. Referring to Figure 4, the image numbers of the medical sample images and their corresponding true (T)/false (F) confirmation columns are displayed for the review results, so that the doctor can confirm that the medical sample images are calculated by the deep learning algorithm. Is it trustworthy? For example, in this embodiment, if the medical sample image is calculated by the deep learning algorithm to show the correct type, the doctor selects the medical sample image to be classified as true (T) in the true (T)/false (F) confirmation column ; On the contrary, the doctor selects the medical sample image to be classified as false (F) in the true (T)/false (F) confirmation column. Also in this embodiment, the predetermined number of the medical sample images can be set to at least 100 and there are at least 50 normal types of the medical sample images in the predetermined number.
於另一實施例中，訓練深度學習演算法更可以包含步驟S37，係利用複核結果優化深度學習演算法。 In another embodiment, training the deep learning algorithm may further include step S37, which is to optimize the deep learning algorithm using the review result.
回到圖1，接續步驟S12，其係執行一深度學習演算法(deep learn algorithm)演算醫學影像，以自醫學影像擷取特徵點(feature point)而建立分布狀態。藉由深度學習演算法演算特徵點，以在分布狀態顯示符合類型的機率分布。其中，深度學習演算法為卷積神經網路演算(Convolutional neural network)，在該深度學習演算法演算醫學影像之後，預測該醫學影像屬於該等類型之一類或多類。 Returning to Fig. 1, following step S12, a deep learning algorithm is executed to calculate the medical image to extract feature points from the medical image to establish a distribution state. The feature points are calculated by the deep learning algorithm to display the probability distribution that matches the type in the distribution state. The deep learning algorithm is a convolutional neural network. After the deep learning algorithm calculates the medical image, it is predicted that the medical image belongs to one or more of these types.
步驟S13，係執行深度學習演算法，以根據分布狀態自複數類型選擇一個或多個類型，即是將醫學影像分類為一個或多個類型。一併可以參考圖5，係顯示某一醫學影像經過深度學習演算法之後，其特徵點在分布狀態上的機率分佈。以圖5為例，機率分布的數值分佈在各類型的狀態，但是在類型Type 1C與類型Type 1D的數值是較高的，故深度學習演算法預測醫學影像預設醫學影像是屬於類型Type 1C或是可能是類型Type 1C與類型Type 1D。換言之，於一實施例中，可以設定當機率分布的數值超過預定閥值，則特徵點對應的醫學影像被分類到該等類型之一類或多類。
In step S13, a deep learning algorithm is executed to select one or more types from the plural types according to the distribution state, that is, to classify the medical image into one or more types. Also refer to Figure 5, which shows the probability distribution of the feature points of a certain medical image after the deep learning algorithm. Taking Figure 5 as an example, the probability distribution values are distributed in various types of states, but the values in
回到圖1，步驟S14，係利用熱點影像演算法演算醫學影像，以在醫學影像標記骨頭之一指定部位，如同圖2(b)所示。 Returning to Fig. 1, step S14 is to use the hotspot image algorithm to calculate the medical image to mark a designated part of the bone in the medical image, as shown in Fig. 2(b).
步驟S15，係顯示經標記的指定部位的醫學影像及其相應的該類型，以供提供一輔助診斷資訊。 Step S15 is to display the marked medical image of the designated part and its corresponding type, so as to provide auxiliary diagnosis information.
請參考圖6，係本創作第二實施例之人工智慧輔助骨頭醫學影像判讀系統的方塊圖。在圖6中，人工智慧輔助骨頭醫學影像判讀系統10包含一輸入單元12、一處理單元14與一輸出單元16。
Please refer to FIG. 6, which is a block diagram of the artificial intelligence assisted bone medical image interpretation system according to the second embodiment of the present creation. In FIG. 6, the artificial intelligence-assisted bone medical
輸入單元12接收一醫學影像MIMG。其中，醫學影像MIMG相關於一骨頭2。
The
處理單元14連接輸入單元12。處理單元14執行一深度學習演算法DLA以演算醫學影像MIMG，而自醫學影像MIMG擷取一特徵點以建立一分布狀態DS，且深度學習演算法DLA又根據分布狀態DS自複數類型CLS選擇一個或多個類型CLS，另處理單元14執行一熱點影像演算法HPA演算醫學影像MIMG，以在醫學影像MIMG標記骨頭2之一指定部位22。
The
輸出單元16連接處理單元14。輸出單元16輸出已標記的骨頭2之指定部位22的醫學影像MIMG’與被選擇的類型CLS或等類型CLS。
The
請參考圖7，係本創作第三實施例之人工智慧輔助骨頭醫學影像判讀系統的方塊圖。在圖7中，人工智慧輔助骨頭醫學影像判讀系統10’包含第二實施例的輸入單元12、處理單元14與輸出單元16之外，更包含回饋模組18。
Please refer to FIG. 7, which is a block diagram of the artificial intelligence-assisted bone medical image interpretation system according to the third embodiment of the present creation. In FIG. 7, the artificial intelligence-assisted bone medical image interpretation system 10' includes the
輸入單元12、處理單元14與輸出單元16如前所述，於此不贅述。
The
回饋模組18連接處理單元14。回饋模組18傳送已標記的骨頭之指定部位的醫學影像及/或被選擇的類型或該等類型的回饋訊息以優化深度學習演算法。
The
值得注意的是，前述個實施例中，其醫生與醫療影像除可以在例如同一醫療場域進行實施之外，也可以透過雲端實現跨醫療場域，例如全球各地的醫生可以根據其回饋的結果以強化與優化深度學習演算法。 It is worth noting that in the foregoing embodiment, in addition to the implementation of doctors and medical images in the same medical field, for example, they can also be implemented across medical fields through the cloud. For example, doctors from all over the world can use the results of their feedback. To strengthen and optimize deep learning algorithms.
本創作在上文中已以較佳實施例揭露，然熟習本項技術者應理解的是，實施例僅用於描繪本創作，而不應解讀為限制本創作之範圍。應注意的是，舉凡與實施例等效之變化與置換，均應設為涵蓋於本創作之範疇內。因此，本創作之保護範圍當以申請專利範圍所界定者為準。 This creation has been disclosed in the preferred embodiments above, but those familiar with this technology should understand that the embodiments are only used to describe the creation, and should not be interpreted as limiting the scope of the creation. It should be noted that all changes and replacements equivalent to the embodiments should be included in the scope of this creation. Therefore, the scope of protection of this creation shall be subject to the scope of the patent application.
2:骨頭 2: bones
22:指定部位 22: Designated part
10:人工智慧輔助骨頭醫學影像判讀系統 10: Artificial intelligence assisted bone medical imaging interpretation system
12:輸入單元 12: Input unit
14:處理單元 14: Processing unit
16:輸出單元 16: output unit
MIMG、MIMG':醫學影像 MIMG, MIMG': medical imaging
DLA:深度學習演算法 DLA: Deep Learning Algorithm
CLS:類型 CLS: Type
HPA:熱點影像演算法 HPA: Hotspot image algorithm
Claims (16)
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202063081346P | 2020-09-22 | 2020-09-22 | |
| US63/081346 | 2020-09-22 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| TWM616670U true TWM616670U (en) | 2021-09-11 |
Family
ID=78778776
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| TW109139990A TWI758952B (en) | 2020-09-22 | 2020-11-16 | A method of assisted interpretation of bone medical images with artificial intelligence and system thereof |
| TW109215099U TWM616670U (en) | 2020-09-22 | 2020-11-16 | A system of assisted interpretation of bone medical images with artificial intelligence |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| TW109139990A TWI758952B (en) | 2020-09-22 | 2020-11-16 | A method of assisted interpretation of bone medical images with artificial intelligence and system thereof |
Country Status (1)
| Country | Link |
|---|---|
| TW (2) | TWI758952B (en) |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103607947B (en) * | 2010-08-13 | 2017-07-18 | 史密夫和内修有限公司 | The detection of anatomic landmark |
| US10409235B2 (en) * | 2014-11-12 | 2019-09-10 | Siemens Healthcare Gmbh | Semantic medical image to 3D print of anatomic structure |
| TWI701680B (en) * | 2018-08-19 | 2020-08-11 | 長庚醫療財團法人林口長庚紀念醫院 | Method and system of analyzing medical images |
| CN110739073A (en) * | 2019-10-18 | 2020-01-31 | 中国医学科学院北京协和医院 | Computer intelligent diagnosis system for osteogenesis imperfecta |
-
2020
- 2020-11-16 TW TW109139990A patent/TWI758952B/en active
- 2020-11-16 TW TW109215099U patent/TWM616670U/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| TW202213379A (en) | 2022-04-01 |
| TWI758952B (en) | 2022-03-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2022500148A (en) | Intraoperative surgical guidance system and usage of artificial intelligence | |
| US12046357B2 (en) | Methods for determining one or more captured images used in a machine learning assessment of an animal | |
| CN109903848A (en) | Fracture around joint clinic intelligent decision support system | |
| Nallamothu et al. | Artificial intelligence in orthopedics: A concise review | |
| Ba | Medical sports rehabilitation deep learning system of sports injury based on MRI image analysis | |
| Padash et al. | An overview of machine learning in orthopedic surgery: an educational paper | |
| TWM616670U (en) | A system of assisted interpretation of bone medical images with artificial intelligence | |
| CN112036431A (en) | Method for classifying fracture types and method for constructing fracture identification model | |
| CN109979590A (en) | Peri-knee fractures clinic intelligent decision support system | |
| JP7092218B1 (en) | Medical information management equipment and medical information management program | |
| Zou et al. | Detection of whole body bone fractures based on improved yolov7 | |
| Chabot et al. | Machine learning: the what, why, and how | |
| CN109903849A (en) | Shoulder joint surrounding fracture clinic intelligent decision support system | |
| CN109920537A (en) | Elbow joint surrounding fracture clinic intelligent decision support system | |
| CN110021429A (en) | Ankle-joint surrounding fracture clinic intelligent decision support system | |
| Haq et al. | Fusion of clinical data: A case study to predict the type of treatment of bone fractures | |
| Nehrer et al. | AI in musculoskeletal radiology | |
| CN109903847A (en) | Wrist joint surrounding fracture clinic intelligent decision support system | |
| CN109859842A (en) | Hip joint surrounding fracture clinic intelligent decision support system | |
| Ling et al. | Machine learning-based segmentation of images to diagnose the orthopedic diseases and to guide the orthopedic surgeries | |
| Bottomley et al. | Arthroscopic Meniscectomy vs Meniscal Repair: Comparison of Clinical Outcomes | |
| KR102598233B1 (en) | System and method for providing medical information based on deep learning targeting medical images | |
| WO2022181154A1 (en) | Assessment system, assessment system control method, and control program | |
| US20230404673A1 (en) | Apparatus, system, and method for generating patient-specific implants and/or instrumentation for osteotomies | |
| US20230116332A1 (en) | System and method for diagnosing muscle and bone related disorders |